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Lark Workflow Meeting Todo

Community
liangdabiao
lark-workflow-meeting-todo

会议待办自动追踪:从飞书妙记中提取待办事项,通过飞书消息确认后创建飞书任务,定期检查完成情况。当用户需要'会议待办'、'会后的待办'、'追踪待办'、'提取会议行动项'、'整理会议纪要待办'时使用。

Overview

Publisherliangdabiao
Repositorylark-workflow-feishu-cli
Skill namelark-workflow-meeting-todo
Stars
68
Forks
10
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by liangdabiao on GitHub. Read the source before you install it.

Installation

Install the Lark Workflow Meeting Todo AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/liangdabiao/lark-workflow-feishu-cli/tree/main/.claude/skills/lark-workflow-meeting-todo
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/liangdabiao/lark-workflow-feishu-cli.git /tmp/lark-workflow-feishu-cli
mkdir -p .claude/skills
cp -r /tmp/lark-workflow-feishu-cli/.claude/skills/lark-workflow-meeting-todo .claude/skills/lark-workflow-meeting-todo
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lark Workflow Meeting Todo in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Lark Workflow Meeting Todo on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Lark Workflow Meeting Todo is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

会议待办自动追踪工作流

CRITICAL — 开始前 MUST 先用 Read 工具读取 ../lark-shared/SKILL.md,其中包含认证、权限处理

适用场景

  • "帮我整理最近会议的待办事项" / "会议后有什么要做的"
  • "提取这个会议的行动项" / "会上答应了什么事"
  • "检查会议待办的完成情况" / "会议承诺兑现了没"
  • "把会议待办同步到飞书任务"

前置条件

仅支持 user 身份。执行前确保已授权:

bash
lark-cli auth login --domain vc,task,im
lark-cli auth login --domain vc,drive   # 读取纪要文档正文

工作流

{时间范围}
  │
  ├── vc +search ──────────────► 会议列表(meeting_ids)
  │
  ├── vc +notes ───────────────► 纪要文档 tokens
  │
  ├── docs +fetch ─────────────► 读取纪要文档内容(提取待办)
  │     或
  ├── minutes minutes get ─────► 读取妙记 AI 产物(待办、总结)
  │
  ▼
AI 分析提取待办列表
  ├── 我方待办(我答应做的)
  └── 对方待办(对方承诺做的)
          │
          ▼
im +messages-send ──► 发送确认请求到用户
          │
          ▼
task +create ──► 确认后创建飞书任务
          │
          ▼
base +record-upsert ──► 记录追踪状态

模式一:批量提取(指定时间范围)

Step 1: 确定时间范围

默认过去 7 天。推断规则:"今天"→当天,"这周"→本周一~now,"昨天"→昨天整天。

日期转换必须调用系统命令(如 date),不要心算。

Step 2: 查询会议记录

bash
lark-cli vc +search --start "<YYYY-MM-DD>" --end "<YYYY-MM-DD>" --format json --page-size 30
  • 搜索时间范围最大为 1 个月,超出需拆分查询
  • 有 page_token 时必须继续翻页,收集所有 id 字段
  • 记录每个会议的 meeting_id、subject、start_time

Step 3: 获取纪要产物

bash
# 批量获取会议纪要信息(单次最多 50 个 meeting_id)
lark-cli vc +notes --meeting-ids "id1,id2,...,idN"

返回结果包含:

  • note_doc_token — 纪要文档 token(含 AI 总结、待办)
  • verbatim_doc_token — 逐字稿文档 token
  • minute_token — 妙记 token

对于每个会议纪要,提取待办内容:

方式 A:从纪要文档中提取(推荐,内容更丰富)

bash
lark-cli docs +fetch --doc "<note_doc_token>"

方式 B:从妙记 AI 产物中提取(更快)

bash
lark-cli minutes minutes get --params '{"minute_token": "<minute_token>"}'
lark-cli vc +notes --minute-tokens "<minute_token>"

Step 4: AI 分析提取待办

阅读纪要文档内容后,AI 从中提取两类待办:

我方待办(我答应别人要做的):

  • 关键词模式:"我会..."、"我负责..."、"我来跟进..."、"回头我发给你..."、"我回头发邮件..."
  • 提取:待办内容、涉及对象、截止暗示

对方待办(别人答应要做的):

  • 关键词模式:"他说..."、"对方承诺..."、"XX 会提供..."、"下周给回复..."
  • 提取:待办内容、承诺人、截止暗示

输出格式:

## 会议待办提取结果

### 会议:{会议主题}({日期})

#### 我方待办
| # | 待办内容 | 涉及对象 | 建议截止日 |
|---|---------|---------|-----------|
| 1 | 把报告发给张三 | 张三 | 本周五 |

#### 对方待办
| # | 待办内容 | 承诺人 | 建议截止日 |
|---|---------|--------|-----------|
| 1 | 提供设计稿 | 李四 | 下周三 |

Step 5: 发送确认请求

将提取的待办以消息发送给用户确认:

bash
lark-cli im +messages-send --chat-id "<chat_id>" --msg-type interactive --content '<飞书卡片JSON>'

卡片内容包含:

  • 每条待办的简述
  • "确认创建任务" / "跳过" 按钮
  • "全部确认" / "全部跳过" 快捷操作

Step 6: 创建飞书任务

用户确认后,为每条待办创建飞书任务:

bash
lark-cli task +create --summary "<待办内容>" --due "<截止日>" --description "来源会议:{会议主题}({日期})"

Step 7: 记录追踪状态

将待办信息记录到多维表格(需先初始化 Base 表,见"数据初始化"部分):

bash
lark-cli base +record-upsert --table-id "<table_id>" --json '{"字段名":"值"}'

模式二:单会议提取

用户指定某个会议时:

bash
# 通过会议标题搜索
lark-cli vc +search --keyword "<会议主题>" --start "<date>" --end "<date>"

后续流程与模式一相同,但只处理这一个会议。


模式三:检查完成情况

Step 1: 获取追踪记录

bash
lark-cli base +record-list --table-id "<table_id>" --filter '...确认状态 = "已确认"'

Step 2: 检查任务状态

bash
lark-cli task +get-my-tasks --query "<待办关键词>"

Step 3: 验证"发邮件"类承诺

如果待办内容涉及"发邮件",检查邮件是否已发送:

bash
lark-cli mail +messages-search --query "<邮件关键词>" --from "me"

降级说明:如果用户未启用飞书邮箱(mail 命令返回错误码 1230003),邮件验证步骤跳过,在追踪状态中标注"邮件验证不可用,需人工确认"。

Step 4: 更新追踪状态

bash
lark-cli base +record-upsert --table-id "<table_id>" --json '{"字段名":"值"}'

模式四:自动归档

超过 14 天未完成的待办自动标记为"已归档":

bash
lark-cli base +record-list --table-id "<table_id>" --filter '...确认状态 = "已确认" AND 创建时间 < 14天前'

对每条记录更新状态为"已归档",并通过飞书消息提醒用户。


数据初始化

首次使用时,创建多维表格追踪数据:

Base 表结构

表名:会议待办追踪

字段名类型说明
会议主题文本来源会议的标题
会议日期日期会议时间
待办内容文本提取的待办描述
待办类型单选我方待办 / 对方待办
负责人/承诺人文本谁来做
建议截止日日期根据上下文推断
确认状态单选待确认 / 已确认 / 已跳过 / 已完成 / 已归档
关联任务ID文本飞书任务的 task_id
会议ID文本vc meeting_id
创建时间日期记录创建时间

初始化命令

bash
# 1. 创建多维表格
# 注意:+table-create 可能部分成功,需捕获错误后用 +field-create 补字段
# 注意:连续创建字段会触发限流(错误码 800004135),每次调用间隔至少 1 秒
lark-cli base +table-create --name "会议待办追踪" --fields '[
  {"name":"会议主题","type":"text"},
  {"name":"会议日期","type":"datetime"},
  {"name":"待办内容","type":"text"},
  {"name":"待办类型","type":"select","multiple":false,"options":[{"name":"我方待办"},{"name":"对方待办"}]},
  {"name":"负责人/承诺人","type":"text"},
  {"name":"建议截止日","type":"datetime"},
  {"name":"确认状态","type":"select","multiple":false,"options":[{"name":"待确认"},{"name":"已确认"},{"name":"已跳过"},{"name":"已完成"},{"name":"已归档"}]},
  {"name":"关联任务ID","type":"text"},
  {"name":"会议ID","type":"text"},
  {"name":"创建时间","type":"datetime"}
]'

# 2. 记录 base_token 和 table_id 到后续使用

首次使用时向用户确认是否创建追踪表。如果用户已有 Base,可以指定已有 Base URL。

限流处理:如果 +table-create 因限流部分失败,表仍会创建成功。此时:

  1. 用 base +table-list 获取 table_id
  2. 用 base +field-create --table-id <id> --json '{"name":"字段名","type":"text"}' 逐个补字段,每次间隔 1-2 秒

学习模式

用户多次拒绝某类待办时,记录拒绝模式:

  • 在多维表中增加"用户偏好"记录
  • 下次提取时,AI 自动过滤相似类型的待办
  • 示例:"会议纪要类记录"被多次拒绝 → 下次自动跳过"记录会议纪要"类待办

权限表

命令所需 scope
vc +searchvc:meeting:read
vc +notesvc:meeting:read
minutes.minutes.getminutes:minutes:readonly
docs +fetchdocx:document:read
task +createtask:task:write
task +get-my-taskstask:task:read
im +messages-sendim:message:send_as_bot
base +record-listbitable:app:read
base +record-upsertbitable:app:write
mail +messages-searchmail:mail:read

参考

Frequently asked questions

What does the Lark Workflow Meeting Todo AI skill do?

会议待办自动追踪:从飞书妙记中提取待办事项,通过飞书消息确认后创建飞书任务,定期检查完成情况。当用户需要'会议待办'、'会后的待办'、'追踪待办'、'提取会议行动项'、'整理会议纪要待办'时使用。

Why use Lark Workflow Meeting Todo on TypingMind?

Because you install it once and use it with any model. Lark Workflow Meeting Todo is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Lark Workflow Meeting Todo in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/liangdabiao/lark-workflow-feishu-cli/tree/main/.claude/skills/lark-workflow-meeting-todo. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Lark Workflow Meeting Todo?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Lark Workflow Meeting Todo?

As many as you like. As long as a model supports skills, you can use Lark Workflow Meeting Todo with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Lark Workflow Meeting Todo AI skill free?

It is published on GitHub by liangdabiao. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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